Data Visualization

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.title[
# Data Visualization
]
.subtitle[
## Usage of unhcthemes package
]

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# Objectives

**About today:**

- Basic of the grammar of graphics and [**`ggplot2`**](https://ggplot2.tidyverse.org/index.html)
- Introduction to the [**`unhcrthemes`**](https://vidonne.github.io/unhcrthemes/) package
- Best practices to create **UNHCR branded** visuals in R, with examples
- What else, dynamic report with the [**`unhcrdown`**](https://github.com/vidonne/unhcrdown)

**Not today:**

- Data import: [`readr`](https://readr.tidyverse.org/), [`readxl`](https://readxl.tidyverse.org/), etc.
- Data manipulation: [`dplyr`](https://dplyr.tidyverse.org/), [`tidyr`](https://tidyr.tidyverse.org/), etc.
- R programming: [R for Data Science](https://r4ds.had.co.nz/), [Advanced R Programming](https://adv-r.hadley.nz/), etc.

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# `ggplot2` and `unhcrthemes`

### Introduction

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# The `ggplot2` package

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- **`ggplot2`** is an R package for declaratively creating graphics

- **`ggplot2`** is an implementation [The Grammar of Graphics](https://link.springer.com/chapter/10.1007/978-3-642-21551-3_13) by Leland Irving

- **The idea** don't start with the final form of the graphic (Excel approach) but **decompose the graphic** into its constituents

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.center[![ggplot2 HEX](data:image/png;base64,#https://raw.githubusercontent.com/tidyverse/ggplot2/main/man/figures/logo.png)]

]

???
You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
What does it take to create a graphic? Data, axis, geometric objects, etc.

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# Structure of `ggplot2`

How a **`ggplot2`** graph is built on the grammar of graphics elements:

1. **Data .primary[`ggplot(data)`]:** The raw data that you want to plot.

2. **Aesthetics .primary[`aes()`]:** Aesthetics mappings of the geometric and statistical objects, such as position, color, size, shape, and transparency

3. **Geometries .primary[`geom_*()`]:** The geometric shapes that will represent the data.

4. **Statistical transformations .primary[`stat_*()`]:** Statistical summaries of the data, such as quantiles, fitted curves, and sums.

5. **Scales .primary[`scale_*()`]:** Maps between the data and the aesthetic dimensions, such as data range to plot width or factor values to colors.

6. **Coordinate system .primary[`coord_*()`]:** The transformation used for mapping data coordinates into the plane of the data rectangle.

7. **Facets .primary[`facet_*()`]:** The arrangement of the data into a grid of plots.

8. **Visual themes .primary[`theme_*()`]:** The overall visual defaults of a plot, such as background, grids, axes, default typeface, sizes and colors.

???
1. Data - without data, you don't have a plot!
2. Mapping - linking variables to graphical properties.
3. Geometries - interpret aesthetics as graphical representations.
4. Statistics - compute/transform numbers for us.
5. Scales - interpret values in data to graphical properties.
6. Coordinates - define physical mapping.
7. Facets - split plot into panels.
8. Theme - what does your plot look like?

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# The `unhcrthemes` package

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1. Adjusted `ggplot2` theme

2. A series of color palette for:

- A **categorical palette** for UNHCR main data visualization colors

- A **categorical palette** for people of concern to UNHCR categories

- A **categorical palette** for geographical regional divisions of UNHCR

- Six **sequential color palettes** for all the main data visualization colors

- Two recommended **diverging color palette**

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.pull-right[

.center[<img src="https://raw.githubusercontent.com/vidonne/unhcrthemes/master/man/figures/unhcrthemes_sticker.png" alt="unhcrthemes HEX" style="max-width:60%">]

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# `ggplot2` and `unhcrthemes`

### In action

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## Setup

A step-by-step on how to build a UNHCR branded chart with `ggplot2` and `unhcrthemes` packages, using an example chart from [Global Trends 2021](https://www.unhcr.org/globaltrends.html).

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**Load the required packages and data**

```r
# Load packages
library(tidyverse)
library(unhcrthemes)

# Load data
displ <- read_csv("data/displaced_pop.csv")
```

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## Data

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```r
*ggplot(data = displ)
```

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???
1. Data - without data, you don't have a plot!
But nothing happens here because we haven't mapped the raw data to anything. SO we just get a empty canvas.

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## Aesthetics

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```r
ggplot(data = displ,
*      aes(x = year, y = num))
```

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]

???
2. Mapping - linking variables to graphical properties.
We have now mapped the year to the x axis and the number displaced to y but we still don't see anything special except the axis value

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## Geoms

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```r
ggplot(data = displ,
       aes(x = year, y = num)) +
* geom_col()
```

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]

---

## Scale

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```r
ggplot(data = displ,
       aes(x = year, y = num)) +
  geom_col() +
* scale_x_continuous(
*   breaks = scales::pretty_breaks(n = 10))
```

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## Scale

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```r
ggplot(data = displ,
       aes(x = year, y = num)) +
  geom_col() +
  scale_x_continuous(
    breaks = scales::pretty_breaks(n = 10)) +
* scale_y_continuous(
*   labels = scales::label_number_si(),
*   expand = expansion(c(0, 0.1)))
```

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## Theme

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Before playing with `unhcthemes` let's add some information on the chart.

```r
ggplot(data = displ,
       aes(x = year, y = num)) +
  geom_col() +
  scale_x_continuous(
    breaks = scales::pretty_breaks(n = 10)) +
  scale_y_continuous(
    labels = scales::label_number_si(), 
    expand = expansion(c(0, 0.1))) +
* labs(title = "People forced to flee worldwide | 2012-2022",
*   caption = "Source: UNHCR Refugee Data Finder")
```

]

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---

## Theme

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```r
ggplot(data = displ,
       aes(x = year, y = num)) +
  geom_col() +
  scale_x_continuous(
    breaks = scales::pretty_breaks(n = 10)) +
  scale_y_continuous(
    labels = scales::label_number_si(), 
    expand = expansion(c(0, 0.1))) +
  labs(title = "People forced to flee worldwide | 2012-2022",
    caption = "Source: UNHCR Refugee Data Finder") +
* theme_unhcr()
```

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]

---

## Theme

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```r
ggplot(data = displ,
       aes(x = year, y = num)) +
  geom_col() +
  scale_x_continuous(
    breaks = scales::pretty_breaks(n = 10)) +
  scale_y_continuous(
    labels = scales::label_number_si(), 
    expand = expansion(c(0, 0.1))) +
  labs(title = "People forced to flee worldwide | 2012-2022",
    caption = "Source: UNHCR Refugee Data Finder") +
* theme_unhcr(grid = "Y",
*             axis_title = FALSE)
```

]

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]

---

## Theme

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```r
ggplot(data = displ,
       aes(x = year, y = num)) +
  geom_col(
*   color = unhcr_pal(n = 1, name = "pal_blue")
    ) +
  scale_x_continuous(
    breaks = scales::pretty_breaks(n = 10)) +
  scale_y_continuous(
    labels = scales::label_number_si(), 
    expand = expansion(c(0, 0.1))) +
  labs(title = "People forced to flee worldwide | 2012-2022",
    caption = "Source: UNHCR Refugee Data Finder") +
  theme_unhcr(grid = "Y",
              axis_title = FALSE) 
```

]

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]

???
Is color the right property?
Also notice that we haven't mapped the color to anything but we're just setting it.

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## Theme

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```r
ggplot(data = displ,
       aes(x = year, y = num)) +
  geom_col(
*   fill = unhcr_pal(n = 1, name = "pal_blue")
    ) +
  scale_x_continuous(
    breaks = scales::pretty_breaks(n = 10)) +
  scale_y_continuous(
    labels = scales::label_number_si(), 
    expand = expansion(c(0, 0.1))) +
  labs(title = "People forced to flee worldwide | 2012-2022",
    caption = "Source: UNHCR Refugee Data Finder") +
  theme_unhcr(grid = "Y",
              axis_title = FALSE) 
```

]

.pull-right[

]

???
Is color the right property?
Also notice that we haven't mapped the color to anything but we're just setting it.

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## Theme

.pull-left[

```r
ggplot(data = displ,
       aes(x = year, y = num,
*          fill = pop)) +
  geom_col() +
  scale_x_continuous(
    breaks = scales::pretty_breaks(n = 10)) +
  scale_y_continuous(
    labels = scales::label_number_si(), 
    expand = expansion(c(0, 0.1))) +
  labs(title = "People forced to flee worldwide | 2012-2022",
    caption = "Source: UNHCR Refugee Data Finder") +
  theme_unhcr(grid = "Y",
              axis_title = FALSE) 
```

]

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]

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# Thank you

### Questions?